We decided not to include the regularisation-based binarization approach [1]}, which addresses the activation binarization problem, in our benchmark. We have also not included in our benchmark improvements of BC methods which have been proposed in [2]}, [3]}, [4]}, [5]}, [6]}; these methods are all based on the STE [7]} optimizer to update quantized weights. These methods have been shown to be outperformed by AdaBin [8]} and ReacNet [9]}. The latter are currently SOTA methods for energy-friendly inference on the ImageNet dataset. Note that these binary approaches still have a gap in terms of full precision performance, which needs to be addressed by modifying the NN structure [9]}. For ReacNet and AdaBin, we have reported the best results of [8]} for ResNet-18 on ImageNet.
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